Data Products

LLM Training Data

High-quality LLM training data , supervised fine-tuning, RLHF, chain-of-thought demonstrations, and adversarial red teaming for frontier model development.

Large language models are trained on human data at every stage of development: pre-training corpora, supervised fine-tuning demonstrations, RLHF preference rankings, evaluation benchmarks, and red team adversarial prompts. The quality of that LLM training data is the primary variable that determines what a model learns and how reliably it performs.

Appen provides specialist LLM training data across the full development pipeline, from initial fine-tuning demonstrations through to the expert preference feedback and adversarial testing that alignment requires.

LLM Training Data by Stage

Supervised Fine-Tuning Data

Human-authored ideal responses and demonstrations for supervised fine-tuning across instruction following, domain-specific tasks, and multi-turn dialogue. SFT data quality sets the baseline that all subsequent alignment training builds on.

RLHF and Preference Data

Expert preference rankings, comparative evaluations, and nuanced feedback from verified subject matter experts across medicine, law, science, and finance for reinforcement learning with human feedback. SME-quality preference data is what separates frontier models from general-purpose baselines.

Frontier Model Alignment Data

Chain-of-thought reasoning traces, adversarial red teaming, and knowledge rubric design for teams building at the frontier of model capability and safety. Visit the full frontier model alignment hub for the complete capability set.

Agentic AI Training Data

Golden trajectories, task verifier design, RL environment builds, and failure mode analysis for teams training agentic AI systems. Agentic data requirements go beyond standard LLM fine-tuning and require contributor expertise in task execution as well as output evaluation.

LLM Evaluation Data

Evaluation is as important as training. Appen's LLM evaluation benchmarks and model integrity services provide the human-verified evaluation data that automated benchmarks cannot replace. Hallucination detection, preference evaluation, and bias assessment all require human judgment at the precision that LLM outputs now require.

Related Resources

Blog

Unlocking the Power of Human Feedback: Benefits of RLHF

Reinforcement learning with human feedback (RLHF) is a cutting-edge technique that has been gaining popularity in recent years as a means of improving the performance of

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